Overview

Dataset statistics

Number of variables19
Number of observations16
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory160.0 B

Variable types

Categorical2
Numeric17

Alerts

Age is highly overall correlated with PlayerHigh correlation
Tkl is highly overall correlated with TklW and 11 other fieldsHigh correlation
TklW is highly overall correlated with Tkl and 11 other fieldsHigh correlation
Def 3rd is highly overall correlated with Tkl and 10 other fieldsHigh correlation
Mid 3rd is highly overall correlated with Tkl and 9 other fieldsHigh correlation
Att 3rd is highly overall correlated with Sh and 1 other fieldsHigh correlation
Tkl.1 is highly overall correlated with Tkl and 12 other fieldsHigh correlation
Att is highly overall correlated with Tkl and 11 other fieldsHigh correlation
Tkl% is highly overall correlated with PlayerHigh correlation
Lost is highly overall correlated with Tkl and 11 other fieldsHigh correlation
Blocks is highly overall correlated with Tkl and 10 other fieldsHigh correlation
Sh is highly overall correlated with Att 3rd and 1 other fieldsHigh correlation
Pass is highly overall correlated with Tkl and 10 other fieldsHigh correlation
Int is highly overall correlated with Tkl and 12 other fieldsHigh correlation
Tkl+Int is highly overall correlated with Tkl and 11 other fieldsHigh correlation
Clr is highly overall correlated with Tkl and 11 other fieldsHigh correlation
Err is highly overall correlated with Int and 1 other fieldsHigh correlation
Player is highly overall correlated with Age and 17 other fieldsHigh correlation
90s is highly overall correlated with Tkl.1 and 2 other fieldsHigh correlation
Player is uniformly distributedUniform
90s is uniformly distributedUniform
Player has unique valuesUnique
Tkl% has unique valuesUnique
Err has 2 (12.5%) zerosZeros

Reproduction

Analysis started2023-02-11 16:23:50.770824
Analysis finished2023-02-11 16:24:17.580029
Duration26.81 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

Player
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
England
 
1
EnglandOpp
 
1
France
 
1
FranceOpp
 
1
Germany
 
1
Other values (11)
11 

Length

Max length14
Median length10
Mean length7.875
Min length3

Characters and Unicode

Total characters126
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st rowEngland
2nd rowEnglandOpp
3rd rowFrance
4th rowFranceOpp
5th rowGermany

Common Values

ValueCountFrequency (%)
England 1
 
6.2%
EnglandOpp 1
 
6.2%
France 1
 
6.2%
FranceOpp 1
 
6.2%
Germany 1
 
6.2%
GermanyOpp 1
 
6.2%
Italy 1
 
6.2%
ItalyOpp 1
 
6.2%
Netherlands 1
 
6.2%
Opponent Total 1
 
6.2%
Other values (6) 6
37.5%

Length

2023-02-11T11:24:17.650045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
england 1
 
5.9%
opponent 1
 
5.9%
usa 1
 
5.9%
swedenopp 1
 
5.9%
sweden 1
 
5.9%
norwayopp 1
 
5.9%
norway 1
 
5.9%
total 1
 
5.9%
netherlands 1
 
5.9%
englandopp 1
 
5.9%
Other values (7) 7
41.2%

Most occurring characters

ValueCountFrequency (%)
p 16
12.7%
n 13
 
10.3%
a 12
 
9.5%
e 11
 
8.7%
O 8
 
6.3%
r 7
 
5.6%
l 6
 
4.8%
y 6
 
4.8%
t 5
 
4.0%
d 5
 
4.0%
Other values (17) 37
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 97
77.0%
Uppercase Letter 28
 
22.2%
Space Separator 1
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 16
16.5%
n 13
13.4%
a 12
12.4%
e 11
11.3%
r 7
7.2%
l 6
 
6.2%
y 6
 
6.2%
t 5
 
5.2%
d 5
 
5.2%
w 4
 
4.1%
Other values (6) 12
12.4%
Uppercase Letter
ValueCountFrequency (%)
O 8
28.6%
S 4
14.3%
N 3
 
10.7%
E 2
 
7.1%
U 2
 
7.1%
I 2
 
7.1%
G 2
 
7.1%
F 2
 
7.1%
A 2
 
7.1%
T 1
 
3.6%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 125
99.2%
Common 1
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 16
12.8%
n 13
 
10.4%
a 12
 
9.6%
e 11
 
8.8%
O 8
 
6.4%
r 7
 
5.6%
l 6
 
4.8%
y 6
 
4.8%
t 5
 
4.0%
d 5
 
4.0%
Other values (16) 36
28.8%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 16
12.7%
n 13
 
10.3%
a 12
 
9.5%
e 11
 
8.7%
O 8
 
6.3%
r 7
 
5.6%
l 6
 
4.8%
y 6
 
4.8%
t 5
 
4.0%
d 5
 
4.0%
Other values (17) 37
29.4%

Age
Real number (ℝ)

Distinct13
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.09375
Minimum25.5
Maximum28.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:17.737067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum25.5
5-th percentile25.8
Q126.65
median26.95
Q327.8
95-th percentile28.575
Maximum28.8
Range3.3
Interquartile range (IQR)1.15

Descriptive statistics

Standard deviation0.90145715
Coefficient of variation (CV)0.033271775
Kurtosis-0.25020261
Mean27.09375
Median Absolute Deviation (MAD)0.65
Skewness0.20152057
Sum433.5
Variance0.812625
MonotonicityNot monotonic
2023-02-11T11:24:17.817084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
27.8 2
12.5%
26.9 2
12.5%
27.2 2
12.5%
27.9 1
 
6.2%
25.5 1
 
6.2%
26.7 1
 
6.2%
26.5 1
 
6.2%
25.9 1
 
6.2%
26.1 1
 
6.2%
26.8 1
 
6.2%
Other values (3) 3
18.8%
ValueCountFrequency (%)
25.5 1
6.2%
25.9 1
6.2%
26.1 1
6.2%
26.5 1
6.2%
26.7 1
6.2%
26.8 1
6.2%
26.9 2
12.5%
27 1
6.2%
27.2 2
12.5%
27.8 2
12.5%
ValueCountFrequency (%)
28.8 1
6.2%
28.5 1
6.2%
27.9 1
6.2%
27.8 2
12.5%
27.2 2
12.5%
27 1
6.2%
26.9 2
12.5%
26.8 1
6.2%
26.7 1
6.2%
26.5 1
6.2%

90s
Categorical

HIGH CORRELATION  UNIFORM 

Distinct4
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
7.0
5.3
5.0
7.3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7.0
2nd row7.0
3rd row5.3
4th row5.3
5th row5.0

Common Values

ValueCountFrequency (%)
7.0 4
25.0%
5.3 4
25.0%
5.0 4
25.0%
7.3 4
25.0%

Length

2023-02-11T11:24:17.909105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-11T11:24:18.011127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
7.0 4
25.0%
5.3 4
25.0%
5.0 4
25.0%
7.3 4
25.0%

Most occurring characters

ValueCountFrequency (%)
. 16
33.3%
7 8
16.7%
0 8
16.7%
5 8
16.7%
3 8
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
66.7%
Other Punctuation 16
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 8
25.0%
0 8
25.0%
5 8
25.0%
3 8
25.0%
Other Punctuation
ValueCountFrequency (%)
. 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 16
33.3%
7 8
16.7%
0 8
16.7%
5 8
16.7%
3 8
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 16
33.3%
7 8
16.7%
0 8
16.7%
5 8
16.7%
3 8
16.7%

Tkl
Real number (ℝ)

Distinct15
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.5625
Minimum81
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:18.099148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile85.5
Q1104.75
median118.5
Q3140
95-th percentile179.75
Maximum182
Range101
Interquartile range (IQR)35.25

Descriptive statistics

Standard deviation32.130917
Coefficient of variation (CV)0.25387391
Kurtosis-0.61750445
Mean126.5625
Median Absolute Deviation (MAD)17
Skewness0.58971361
Sum2025
Variance1032.3958
MonotonicityNot monotonic
2023-02-11T11:24:18.187168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
115 2
 
12.5%
135 1
 
6.2%
128 1
 
6.2%
106 1
 
6.2%
120 1
 
6.2%
101 1
 
6.2%
81 1
 
6.2%
182 1
 
6.2%
155 1
 
6.2%
87 1
 
6.2%
Other values (5) 5
31.2%
ValueCountFrequency (%)
81 1
6.2%
87 1
6.2%
95 1
6.2%
101 1
6.2%
106 1
6.2%
115 2
12.5%
117 1
6.2%
120 1
6.2%
128 1
6.2%
131 1
6.2%
ValueCountFrequency (%)
182 1
6.2%
179 1
6.2%
178 1
6.2%
155 1
6.2%
135 1
6.2%
131 1
6.2%
128 1
6.2%
120 1
6.2%
117 1
6.2%
115 2
12.5%

TklW
Real number (ℝ)

Distinct15
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.4375
Minimum45
Maximum114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:18.272187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile48.75
Q168.75
median73.5
Q386.25
95-th percentile112.5
Maximum114
Range69
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation20.006561
Coefficient of variation (CV)0.25835753
Kurtosis-0.27240819
Mean77.4375
Median Absolute Deviation (MAD)10.5
Skewness0.44679337
Sum1239
Variance400.2625
MonotonicityNot monotonic
2023-02-11T11:24:18.356206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
71 2
 
12.5%
76 1
 
6.2%
83 1
 
6.2%
50 1
 
6.2%
73 1
 
6.2%
72 1
 
6.2%
45 1
 
6.2%
114 1
 
6.2%
96 1
 
6.2%
60 1
 
6.2%
Other values (5) 5
31.2%
ValueCountFrequency (%)
45 1
6.2%
50 1
6.2%
60 1
6.2%
62 1
6.2%
71 2
12.5%
72 1
6.2%
73 1
6.2%
74 1
6.2%
76 1
6.2%
78 1
6.2%
ValueCountFrequency (%)
114 1
6.2%
112 1
6.2%
102 1
6.2%
96 1
6.2%
83 1
6.2%
78 1
6.2%
76 1
6.2%
74 1
6.2%
73 1
6.2%
72 1
6.2%

Def 3rd
Real number (ℝ)

Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.9375
Minimum36
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:18.449228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile39
Q148.5
median55.5
Q385.75
95-th percentile98.5
Maximum106
Range70
Interquartile range (IQR)37.25

Descriptive statistics

Standard deviation22.376978
Coefficient of variation (CV)0.34998207
Kurtosis-1.0274435
Mean63.9375
Median Absolute Deviation (MAD)12
Skewness0.65443555
Sum1023
Variance500.72917
MonotonicityNot monotonic
2023-02-11T11:24:18.530245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
91 2
12.5%
49 2
12.5%
55 1
 
6.2%
61 1
 
6.2%
40 1
 
6.2%
84 1
 
6.2%
44 1
 
6.2%
56 1
 
6.2%
68 1
 
6.2%
36 1
 
6.2%
Other values (4) 4
25.0%
ValueCountFrequency (%)
36 1
6.2%
40 1
6.2%
44 1
6.2%
47 1
6.2%
49 2
12.5%
50 1
6.2%
55 1
6.2%
56 1
6.2%
61 1
6.2%
68 1
6.2%
ValueCountFrequency (%)
106 1
6.2%
96 1
6.2%
91 2
12.5%
84 1
6.2%
68 1
6.2%
61 1
6.2%
56 1
6.2%
55 1
6.2%
50 1
6.2%
49 2
12.5%

Mid 3rd
Real number (ℝ)

Distinct15
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.3125
Minimum27
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:18.615264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile28.5
Q138.75
median51
Q362
95-th percentile70
Maximum76
Range49
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation14.425527
Coefficient of variation (CV)0.28671855
Kurtosis-0.80741
Mean50.3125
Median Absolute Deviation (MAD)11.5
Skewness0.0060955603
Sum805
Variance208.09583
MonotonicityNot monotonic
2023-02-11T11:24:18.702284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
62 2
 
12.5%
50 1
 
6.2%
49 1
 
6.2%
29 1
 
6.2%
45 1
 
6.2%
53 1
 
6.2%
38 1
 
6.2%
34 1
 
6.2%
76 1
 
6.2%
54 1
 
6.2%
Other values (5) 5
31.2%
ValueCountFrequency (%)
27 1
6.2%
29 1
6.2%
34 1
6.2%
38 1
6.2%
39 1
6.2%
45 1
6.2%
49 1
6.2%
50 1
6.2%
52 1
6.2%
53 1
6.2%
ValueCountFrequency (%)
76 1
6.2%
68 1
6.2%
67 1
6.2%
62 2
12.5%
54 1
6.2%
53 1
6.2%
52 1
6.2%
50 1
6.2%
49 1
6.2%
45 1
6.2%

Att 3rd
Real number (ℝ)

Distinct10
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.3125
Minimum6
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:18.782302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q19.75
median11.5
Q316
95-th percentile17.25
Maximum18
Range12
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation4.0615063
Coefficient of variation (CV)0.32986853
Kurtosis-1.2862803
Mean12.3125
Median Absolute Deviation (MAD)4
Skewness-0.20408413
Sum197
Variance16.495833
MonotonicityNot monotonic
2023-02-11T11:24:18.881324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11 3
18.8%
17 2
12.5%
6 2
12.5%
15 2
12.5%
16 2
12.5%
18 1
 
6.2%
7 1
 
6.2%
12 1
 
6.2%
9 1
 
6.2%
10 1
 
6.2%
ValueCountFrequency (%)
6 2
12.5%
7 1
 
6.2%
9 1
 
6.2%
10 1
 
6.2%
11 3
18.8%
12 1
 
6.2%
15 2
12.5%
16 2
12.5%
17 2
12.5%
18 1
 
6.2%
ValueCountFrequency (%)
18 1
 
6.2%
17 2
12.5%
16 2
12.5%
15 2
12.5%
12 1
 
6.2%
11 3
18.8%
10 1
 
6.2%
9 1
 
6.2%
7 1
 
6.2%
6 2
12.5%

Tkl.1
Real number (ℝ)

Distinct15
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.9375
Minimum25
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:18.986347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile26.5
Q137.75
median44
Q351.5
95-th percentile74.5
Maximum85
Range60
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation16.570933
Coefficient of variation (CV)0.35304252
Kurtosis0.52617517
Mean46.9375
Median Absolute Deviation (MAD)8
Skewness0.89237773
Sum751
Variance274.59583
MonotonicityNot monotonic
2023-02-11T11:24:19.077369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
44 2
 
12.5%
39 1
 
6.2%
42 1
 
6.2%
27 1
 
6.2%
46 1
 
6.2%
25 1
 
6.2%
49 1
 
6.2%
29 1
 
6.2%
71 1
 
6.2%
69 1
 
6.2%
Other values (5) 5
31.2%
ValueCountFrequency (%)
25 1
6.2%
27 1
6.2%
29 1
6.2%
34 1
6.2%
39 1
6.2%
41 1
6.2%
42 1
6.2%
44 2
12.5%
46 1
6.2%
49 1
6.2%
ValueCountFrequency (%)
85 1
6.2%
71 1
6.2%
69 1
6.2%
56 1
6.2%
50 1
6.2%
49 1
6.2%
46 1
6.2%
44 2
12.5%
42 1
6.2%
41 1
6.2%

Att
Real number (ℝ)

Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.1875
Minimum52
Maximum149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:19.176391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile53.5
Q179.25
median87
Q3118.25
95-th percentile148.25
Maximum149
Range97
Interquartile range (IQR)39

Descriptive statistics

Standard deviation30.362738
Coefficient of variation (CV)0.3189782
Kurtosis-0.62434392
Mean95.1875
Median Absolute Deviation (MAD)19.5
Skewness0.49238298
Sum1523
Variance921.89583
MonotonicityNot monotonic
2023-02-11T11:24:19.266411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
87 3
18.8%
116 1
 
6.2%
52 1
 
6.2%
94 1
 
6.2%
54 1
 
6.2%
85 1
 
6.2%
92 1
 
6.2%
64 1
 
6.2%
149 1
 
6.2%
125 1
 
6.2%
Other values (4) 4
25.0%
ValueCountFrequency (%)
52 1
 
6.2%
54 1
 
6.2%
64 1
 
6.2%
71 1
 
6.2%
82 1
 
6.2%
85 1
 
6.2%
87 3
18.8%
92 1
 
6.2%
94 1
 
6.2%
116 1
 
6.2%
ValueCountFrequency (%)
149 1
 
6.2%
148 1
 
6.2%
130 1
 
6.2%
125 1
 
6.2%
116 1
 
6.2%
94 1
 
6.2%
92 1
 
6.2%
87 3
18.8%
85 1
 
6.2%
82 1
 
6.2%

Tkl%
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.25625
Minimum36.2
Maximum57.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:19.618490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum36.2
5-th percentile41.375
Q146.05
median48.4
Q353.4
95-th percentile57.425
Maximum57.5
Range21.3
Interquartile range (IQR)7.35

Descriptive statistics

Standard deviation5.6616804
Coefficient of variation (CV)0.11494339
Kurtosis0.38851281
Mean49.25625
Median Absolute Deviation (MAD)3.55
Skewness-0.48380818
Sum788.1
Variance32.054625
MonotonicityNot monotonic
2023-02-11T11:24:19.700508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
44.8 1
 
6.2%
36.2 1
 
6.2%
51.9 1
 
6.2%
48.9 1
 
6.2%
46.3 1
 
6.2%
51.8 1
 
6.2%
53.3 1
 
6.2%
45.3 1
 
6.2%
47.7 1
 
6.2%
55.2 1
 
6.2%
Other values (6) 6
37.5%
ValueCountFrequency (%)
36.2 1
6.2%
43.1 1
6.2%
44.8 1
6.2%
45.3 1
6.2%
46.3 1
6.2%
47.1 1
6.2%
47.7 1
6.2%
47.9 1
6.2%
48.9 1
6.2%
51.8 1
6.2%
ValueCountFrequency (%)
57.5 1
6.2%
57.4 1
6.2%
55.2 1
6.2%
53.7 1
6.2%
53.3 1
6.2%
51.9 1
6.2%
51.8 1
6.2%
48.9 1
6.2%
47.9 1
6.2%
47.7 1
6.2%

Lost
Real number (ℝ)

Distinct13
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.25
Minimum25
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:19.784527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile28
Q137
median44.5
Q357.75
95-th percentile75
Maximum78
Range53
Interquartile range (IQR)20.75

Descriptive statistics

Standard deviation16.368669
Coefficient of variation (CV)0.33924703
Kurtosis-0.62692923
Mean48.25
Median Absolute Deviation (MAD)8.5
Skewness0.65430651
Sum772
Variance267.93333
MonotonicityNot monotonic
2023-02-11T11:24:19.882549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
48 2
12.5%
74 2
12.5%
37 2
12.5%
25 1
 
6.2%
29 1
 
6.2%
41 1
 
6.2%
43 1
 
6.2%
35 1
 
6.2%
78 1
 
6.2%
56 1
 
6.2%
Other values (3) 3
18.8%
ValueCountFrequency (%)
25 1
6.2%
29 1
6.2%
35 1
6.2%
37 2
12.5%
38 1
6.2%
41 1
6.2%
43 1
6.2%
46 1
6.2%
48 2
12.5%
56 1
6.2%
ValueCountFrequency (%)
78 1
6.2%
74 2
12.5%
63 1
6.2%
56 1
6.2%
48 2
12.5%
46 1
6.2%
43 1
6.2%
41 1
6.2%
38 1
6.2%
37 2
12.5%

Blocks
Real number (ℝ)

Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.1875
Minimum44
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:19.972570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile50.75
Q166.5
median75
Q378.75
95-th percentile84.25
Maximum85
Range41
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation12.001215
Coefficient of variation (CV)0.16858599
Kurtosis0.20799986
Mean71.1875
Median Absolute Deviation (MAD)6
Skewness-0.99680258
Sum1139
Variance144.02917
MonotonicityNot monotonic
2023-02-11T11:24:20.054590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
71 2
12.5%
77 2
12.5%
74 1
 
6.2%
84 1
 
6.2%
44 1
 
6.2%
81 1
 
6.2%
57 1
 
6.2%
53 1
 
6.2%
85 1
 
6.2%
78 1
 
6.2%
Other values (4) 4
25.0%
ValueCountFrequency (%)
44 1
6.2%
53 1
6.2%
57 1
6.2%
59 1
6.2%
69 1
6.2%
71 2
12.5%
74 1
6.2%
76 1
6.2%
77 2
12.5%
78 1
6.2%
ValueCountFrequency (%)
85 1
6.2%
84 1
6.2%
83 1
6.2%
81 1
6.2%
78 1
6.2%
77 2
12.5%
76 1
6.2%
74 1
6.2%
71 2
12.5%
69 1
6.2%

Sh
Real number (ℝ)

Distinct11
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.125
Minimum7
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:20.146609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile8.5
Q114
median17.5
Q321
95-th percentile24.25
Maximum25
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.4144867
Coefficient of variation (CV)0.31617441
Kurtosis-0.63791052
Mean17.125
Median Absolute Deviation (MAD)3.5
Skewness-0.34892001
Sum274
Variance29.316667
MonotonicityNot monotonic
2023-02-11T11:24:20.228628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
21 2
12.5%
24 2
12.5%
18 2
12.5%
17 2
12.5%
14 2
12.5%
9 1
6.2%
7 1
6.2%
10 1
6.2%
15 1
6.2%
25 1
6.2%
ValueCountFrequency (%)
7 1
6.2%
9 1
6.2%
10 1
6.2%
14 2
12.5%
15 1
6.2%
17 2
12.5%
18 2
12.5%
20 1
6.2%
21 2
12.5%
24 2
12.5%
ValueCountFrequency (%)
25 1
6.2%
24 2
12.5%
21 2
12.5%
20 1
6.2%
18 2
12.5%
17 2
12.5%
15 1
6.2%
14 2
12.5%
10 1
6.2%
9 1
6.2%

Pass
Real number (ℝ)

Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.0625
Minimum37
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:20.320650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile38.5
Q145
median55
Q360
95-th percentile69.25
Maximum70
Range33
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.174928
Coefficient of variation (CV)0.18820677
Kurtosis-0.91023928
Mean54.0625
Median Absolute Deviation (MAD)9
Skewness-0.11429128
Sum865
Variance103.52917
MonotonicityNot monotonic
2023-02-11T11:24:20.404669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
45 2
12.5%
59 2
12.5%
65 1
 
6.2%
63 1
 
6.2%
37 1
 
6.2%
57 1
 
6.2%
39 1
 
6.2%
54 1
 
6.2%
56 1
 
6.2%
43 1
 
6.2%
Other values (4) 4
25.0%
ValueCountFrequency (%)
37 1
6.2%
39 1
6.2%
43 1
6.2%
45 2
12.5%
51 1
6.2%
53 1
6.2%
54 1
6.2%
56 1
6.2%
57 1
6.2%
59 2
12.5%
ValueCountFrequency (%)
70 1
6.2%
69 1
6.2%
65 1
6.2%
63 1
6.2%
59 2
12.5%
57 1
6.2%
56 1
6.2%
54 1
6.2%
53 1
6.2%
51 1
6.2%

Int
Real number (ℝ)

Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.9375
Minimum44
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:20.493689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile48.5
Q154.5
median68
Q385.25
95-th percentile95.75
Maximum98
Range54
Interquartile range (IQR)30.75

Descriptive statistics

Standard deviation17.544111
Coefficient of variation (CV)0.25085414
Kurtosis-1.2425457
Mean69.9375
Median Absolute Deviation (MAD)17.5
Skewness0.17448564
Sum1119
Variance307.79583
MonotonicityNot monotonic
2023-02-11T11:24:20.590709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
50 3
18.8%
70 1
 
6.2%
92 1
 
6.2%
56 1
 
6.2%
69 1
 
6.2%
62 1
 
6.2%
44 1
 
6.2%
98 1
 
6.2%
85 1
 
6.2%
67 1
 
6.2%
Other values (4) 4
25.0%
ValueCountFrequency (%)
44 1
 
6.2%
50 3
18.8%
56 1
 
6.2%
62 1
 
6.2%
66 1
 
6.2%
67 1
 
6.2%
69 1
 
6.2%
70 1
 
6.2%
79 1
 
6.2%
85 1
 
6.2%
ValueCountFrequency (%)
98 1
6.2%
95 1
6.2%
92 1
6.2%
86 1
6.2%
85 1
6.2%
79 1
6.2%
70 1
6.2%
69 1
6.2%
67 1
6.2%
66 1
6.2%

Tkl+Int
Real number (ℝ)

Distinct15
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.5
Minimum131
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:20.678729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum131
5-th percentile135.5
Q1161.25
median192.5
Q3225
95-th percentile274.75
Maximum280
Range149
Interquartile range (IQR)63.75

Descriptive statistics

Standard deviation47.720017
Coefficient of variation (CV)0.24284996
Kurtosis-0.84689293
Mean196.5
Median Absolute Deviation (MAD)32
Skewness0.50386961
Sum3144
Variance2277.2
MonotonicityNot monotonic
2023-02-11T11:24:20.769750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
162 2
 
12.5%
205 1
 
6.2%
220 1
 
6.2%
189 1
 
6.2%
151 1
 
6.2%
177 1
 
6.2%
159 1
 
6.2%
131 1
 
6.2%
280 1
 
6.2%
240 1
 
6.2%
Other values (5) 5
31.2%
ValueCountFrequency (%)
131 1
6.2%
137 1
6.2%
151 1
6.2%
159 1
6.2%
162 2
12.5%
177 1
6.2%
189 1
6.2%
196 1
6.2%
197 1
6.2%
205 1
6.2%
ValueCountFrequency (%)
280 1
6.2%
273 1
6.2%
265 1
6.2%
240 1
6.2%
220 1
6.2%
205 1
6.2%
197 1
6.2%
196 1
6.2%
189 1
6.2%
177 1
6.2%

Clr
Real number (ℝ)

Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.75
Minimum53
Maximum184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:20.862771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile77.75
Q196.5
median122
Q3147.25
95-th percentile163
Maximum184
Range131
Interquartile range (IQR)50.75

Descriptive statistics

Standard deviation34.141861
Coefficient of variation (CV)0.28042596
Kurtosis-0.39839597
Mean121.75
Median Absolute Deviation (MAD)25.5
Skewness-0.16989786
Sum1948
Variance1165.6667
MonotonicityNot monotonic
2023-02-11T11:24:20.955793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
98 2
12.5%
122 2
12.5%
148 1
 
6.2%
53 1
 
6.2%
143 1
 
6.2%
92 1
 
6.2%
90 1
 
6.2%
147 1
 
6.2%
156 1
 
6.2%
111 1
 
6.2%
Other values (4) 4
25.0%
ValueCountFrequency (%)
53 1
6.2%
86 1
6.2%
90 1
6.2%
92 1
6.2%
98 2
12.5%
111 1
6.2%
122 2
12.5%
143 1
6.2%
145 1
6.2%
147 1
6.2%
ValueCountFrequency (%)
184 1
6.2%
156 1
6.2%
153 1
6.2%
148 1
6.2%
147 1
6.2%
145 1
6.2%
143 1
6.2%
122 2
12.5%
111 1
6.2%
98 2
12.5%

Err
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0625
Minimum0
Maximum8
Zeros2
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size256.0 B
2023-02-11T11:24:21.044812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1.5
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9482898
Coefficient of variation (CV)0.94462538
Kurtosis5.3020389
Mean2.0625
Median Absolute Deviation (MAD)0.5
Skewness2.0019729
Sum33
Variance3.7958333
MonotonicityNot monotonic
2023-02-11T11:24:21.125831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 6
37.5%
3 3
18.8%
2 3
18.8%
0 2
 
12.5%
8 1
 
6.2%
4 1
 
6.2%
ValueCountFrequency (%)
0 2
 
12.5%
1 6
37.5%
2 3
18.8%
3 3
18.8%
4 1
 
6.2%
8 1
 
6.2%
ValueCountFrequency (%)
8 1
 
6.2%
4 1
 
6.2%
3 3
18.8%
2 3
18.8%
1 6
37.5%
0 2
 
12.5%

Interactions

2023-02-11T11:24:15.735609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:51.233929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:52.676255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:54.285628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:55.734956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:57.275064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:58.993452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:00.424775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:01.833093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:03.446457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:04.818767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:06.252605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:07.875975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:09.426328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:11.094879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:12.509919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:14.015259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:15.825633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:51.320949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:52.764275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:54.372648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:55.823977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:57.358083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:59.081471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:00.510794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:01.918113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:03.525475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:04.933303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:06.339624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:07.964996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:09.512347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:11.178898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:12.598939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:14.103279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:15.908652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:51.400967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:52.845294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:54.453666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:55.904994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:57.438102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:59.163491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:00.588812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:01.995129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:03.601492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:05.021322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-11T11:24:00.157715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:01.574034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:03.197401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:04.534704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:05.995548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:07.629919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:09.151266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:10.587591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:12.249858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:13.732195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:15.472109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:17.023904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:52.489213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:54.112070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:55.552914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:57.092023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:58.805410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:00.246735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:01.661054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:03.282420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:04.620722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:06.082568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:07.713938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:09.241285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:10.675611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:12.336880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:13.827217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:15.560564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:17.113924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:52.580234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:54.197238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:55.641935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:57.177042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:23:58.906431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:00.333756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:01.746073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:03.363438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:04.698739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:06.165585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:07.792957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:09.331306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:11.004860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:12.420899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:13.916237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T11:24:15.645586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-11T11:24:21.233856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
AgeTklTklWDef 3rdMid 3rdAtt 3rdTkl.1AttTkl%LostBlocksShPassIntTkl+IntClrErrPlayer90s
Age1.0000.3420.3580.0690.2630.3570.1720.1660.2610.1270.058-0.1290.1290.3340.3640.1290.3191.0000.276
Tkl0.3421.0000.9070.7740.8290.2970.7720.8830.0180.8350.6320.0220.6230.8340.9650.8030.2351.0000.000
TklW0.3580.9071.0000.8180.6810.0690.8790.9110.2060.8120.7020.1740.5690.8100.9040.8510.1621.0000.445
Def 3rd0.0690.7740.8181.0000.417-0.2370.8360.9110.0630.8860.7430.4110.5770.6820.7530.8330.2701.0000.000
Mid 3rd0.2630.8290.6810.4171.0000.4310.5940.5760.1250.5210.359-0.3440.5310.6860.8350.5310.1941.0000.408
Att 3rd0.3570.2970.069-0.2370.4311.000-0.2270.016-0.3120.074-0.090-0.5460.1160.3030.315-0.0390.1541.0000.328
Tkl.10.1720.7720.8790.8360.594-0.2271.0000.8810.4400.7230.7620.2750.6540.6290.7550.8110.0371.0000.565
Att0.1660.8830.9110.9110.5760.0160.8811.0000.0350.9450.8200.2830.6900.8050.8680.8830.2201.0000.000
Tkl%0.2610.0180.2060.0630.125-0.3120.4400.0351.000-0.2140.2140.0900.158-0.1180.0210.060-0.1721.0000.195
Lost0.1270.8350.8120.8860.5210.0740.7230.945-0.2141.0000.7760.2440.6790.8630.8580.7950.4051.0000.204
Blocks0.0580.6320.7020.7430.359-0.0900.7620.8200.2140.7761.0000.3900.8680.6530.6680.6650.2501.0000.000
Sh-0.1290.0220.1740.411-0.344-0.5460.2750.2830.0900.2440.3901.0000.010-0.031-0.0180.471-0.1851.0000.000
Pass0.1290.6230.5690.5770.5310.1160.6540.6900.1580.6790.8680.0101.0000.6250.6610.4760.3621.0000.363
Int0.3340.8340.8100.6820.6860.3030.6290.805-0.1180.8630.653-0.0310.6251.0000.9370.6530.5201.0000.283
Tkl+Int0.3640.9650.9040.7530.8350.3150.7550.8680.0210.8580.668-0.0180.6610.9371.0000.7660.3751.0000.486
Clr0.1290.8030.8510.8330.531-0.0390.8110.8830.0600.7950.6650.4710.4760.6530.7661.0000.0881.0000.601
Err0.3190.2350.1620.2700.1940.1540.0370.220-0.1720.4050.250-0.1850.3620.5200.3750.0881.0001.0000.000
Player1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
90s0.2760.0000.4450.0000.4080.3280.5650.0000.1950.2040.0000.0000.3630.2830.4860.6010.0001.0001.000

Missing values

2023-02-11T11:24:17.267959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-11T11:24:17.493009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PlayerAge90sTklTklWDef 3rdMid 3rdAtt 3rdTkl.1AttTkl%LostBlocksShPassIntTkl+IntClrErr
0England27.87.013571556218398744.8487496570205988
1EnglandOpp26.97.0128766150174211636.274842163922201483
2France27.85.310671404917275251.9254473756162531
3FranceOpp27.95.31208384297469448.948812457691891432
4Germany25.55.010150444512255446.32957183950151921
5GermanyOpp26.75.01157356536448551.841711754621771223
6Italy26.55.01157268389499253.343772156441591220
7ItalyOpp25.95.08145363411296445.33553104350131901
8Netherlands26.17.31821149176157114947.778851570982801471
9Opponent Total27.27.3155969154106912555.256782553852401562
PlayerAge90sTklTklWDef 3rdMid 3rdAtt 3rdTkl.1AttTkl%LostBlocksShPassIntTkl+IntClrErr
6Italy26.55.01157268389499253.343772156441591220
7ItalyOpp25.95.08145363411296445.33553104350131901
8Netherlands26.17.31821149176157114947.778851570982801471
9Opponent Total27.27.3155969154106912555.256782553852401562
10Norway26.85.38760492711347147.937692445501371111
11NorwayOpp26.95.3956250396448253.73877185967162862
12Sweden28.57.313178476816508757.537761759661971450
13SwedenOpp27.27.31781129667158514857.463831469952731534
14USA28.87.011774495216418747.14659144579196983
15USAOpp27.07.017910210662115613043.174712051862651841